Systems and methods for an improved soil moisture sensor

ABSTRACT

An improved bridge circuitry is presented for improved soil sensor measurements. The improved bridge circuitry may include blocking capacitors, the ability to apply bias voltage, and the substitution of AC meters with DC meters and other improvements.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent application Ser. No. 14/813,083, filed Jul. 29, 2015, which claims the benefit of, and priority to, U.S. Provisional Patent Application Ser. No. 62/030,559, filed Jul. 29, 2014; 62/030,566, filed Jul. 29, 2014; 62/030,568, filed Jul. 29, 2014; and 62/030,573, filed Jul. 29, 2014; and is a continuation-in-part of U.S. patent application Ser. No. 14/813,087, filed Jul. 29, 2015, which claims the benefit of, and priority to, U.S. Provisional Patent Application Ser. No. 62/030,559, filed Jul. 29, 2014; 62/030,566, filed Jul. 29, 2014; 62/030,568, filed Jul. 29, 2014; and 62/030,573, filed Jul. 29, 2014; and is a continuation-in-part of U.S. patent application Ser. No. 14/813,099, filed Jul. 29, 2015, which claims the benefit of, and priority to, U.S. Provisional Patent Application Ser. No. 62/030,559, filed Jul. 29, 2014; 62/030,566, filed Jul. 29, 2014; 62/030,568, filed Jul. 29, 2014; and 62/030,573, filed Jul. 29, 2014; and is a continuation-in-part of U.S. patent application Ser. No. 14/813,107, filed Jul. 29, 2015, which claims the benefit of, and priority to, U.S. Provisional Patent Application Ser. No. 62/030,559, filed Jul. 29, 2014; 62/030,566, filed Jul. 29, 2014; 62/030,568, filed Jul. 29, 2014; and 62/030,573, filed Jul. 29, 2014; the entire disclosures of which are incorporated herein by reference.

BACKGROUND

Field of the Invention

The embodiments described herein generally relate to the field of sensors that measure a material's dielectric constant, conductivity, and temperature, which may be used to determine soil moisture and salinity or other properties of materials such as grain moisture content, cement curing, etc.

Description of the Related Art

As of 2011, the global gardening and outdoor living industry was already worth a staggering $187 billion. In North America alone, sales of plants, seeds, and other garden paraphernalia reached nearly $58 billion. These statistics are compelling evidence of humanity's affinity toward verdant foliage and vibrant blossoms. Indeed, many millions of Americans, whether or not they consider themselves gardening enthusiasts, are engaged in some form of lawn care and gardening activities. There are tens of millions of acres of residential lawns in the United States. Even households that do not have access to residential lawns are keen to install planters, and to find alternative, smaller scale ways to cultivate plants both indoors and outdoors.

But maintaining a garden has also become an increasingly difficult and unattainable endeavor in recent years. First, global climate changes have made it harder to create and sustain the various conditions required for different plants to survive. While weather conditions (e.g., temperature, sunlight, and rainfall) often fluctuate outside of historical norms and sometimes to extremes, plants typically need very steady and specific levels of moisture, heat, and light exposure. Consequently, even seasoned horticulturists are struggling to create and maintain ideal plant habitats. In addition, gardening is also now a much more costly pursuit. Many regions around the world experience severe and chronic drought conditions, and water has become a more precious commodity than ever before. The rates for water in the United States, for instance, have increased drastically in recent years (e.g., 75% between 2000 and 2012) as have the fines and penalties for over- and misusage of water. Meanwhile, the water shortages that plague many parts of the country are not expected to abate any time soon.

Temperamental weather and soaring water costs are just a few of the challenges that overwhelm conventional gardening methods. Under- and overwatering, for example, are a persistent and pricey problem because most households program sprinkler systems according to a static schedule or frequency, causing plants to be watered regardless of current moisture levels in the soil or imminent rainfall. In addition, gardens tend to be individually unique ecosystems, although the differences are often incredibly nuanced and hardly perceptible to the average person. Very often, a plant that flourishes in one location will mysteriously flounder in another. Alice, for example, can fail to grow peonies in her garden even though the same type of plant thrives in her neighbor Bob's yard. This is because conventional approaches to gardening disregard particularized and real-time data with respect to each individual garden ecosystem. Plant care guidelines, which are typically found on placards that accompany commercially available plants, provide only very general recommendations (e.g., light exposure, type of fertilizer, and watering and feeding frequency). But based on these types of instructions alone, it is still nearly impossible to systematically recreate an ideal growing environment. So more often than not, for the average person, successfully growing a plant often entails an interminable and frustrating succession of trial and error.

SUMMARY

Systems and methods are presented for an improved soil moisture sensor that offers reduced temperature drift and reduced sensitivity to detector diode variations and that allows for use of lower cost components in measuring AC signals and wireless monitoring. These and other features, aspects, and embodiments are described below.

BRIEF DESCRIPTION OF THE DRAWINGS

Features, aspects, and embodiments are described in conjunction with the attached drawings.

FIG. 1 is a diagram illustrating an embodiment of a system for dynamically collecting, analyzing, and regulating garden parameters.

FIG. 2 is a diagram illustrating an embodiment of a system for dynamically collecting, analyzing, and regulating garden parameters.

FIG. 3 is a flowchart illustrating an embodiment of a process for dynamically collecting, analyzing, and regulating garden parameters.

FIG. 4A is a diagram illustrating an embodiment of a control system.

FIG. 4B is a flowchart illustrating an embodiment of a process for making plant recommendations.

FIG. 5 illustrates an example of a computing environment including a computing device suitable for use in some embodiments.

FIG. 6 illustrates an example of a sensor platform.

FIG. 7 illustrates an example of a sensor component for measuring the resistance of a soil.

FIG. 8 illustrates an example of an embodiment for collecting garden parameters.

FIG. 9 illustrates an example of an embodiment for performing sensor measurements.

FIG. 10A illustrates an example of a system for measuring AC signals using DC meters.

FIG. 10B illustrates another example of a system for measuring AC signals using DC meters.

FIG. 11 describes a method for measuring soil characteristics.

FIG. 12 shows an example of an application for soil monitoring.

FIG. 13 shows an example of a transmission or receiving coil structure.

FIG. 14 describes a method for tracking sensor locations.

DETAILED DESCRIPTION

The invention may be implemented in numerous ways, including but not limited to implementation as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and a processor, such as a processor configured to execute instructions stored on or provided by a memory coupled to the processor. These implementations, or any other embodiments, may be referred to as techniques. In general, the order of the steps of disclosed processes may be altered unless specified otherwise or otherwise clear from context. Unless stated otherwise, a component such as a processor or a memory described as being configured to perform a task may be implemented as a general component that is temporarily configured to perform the task at a given time or a specific component that is manufactured to perform the task. As used herein, the term “processor” refers to one or more devices, circuits, or processing cores configured to process data, such as computer program instructions.

A detailed description of one or more embodiments is provided below along with accompanying figures. The invention is described in connection with such embodiments, but the invention is not limited to any specific embodiment. The scope of the invention is limited only by the claims and the invention encompasses numerous alternatives, modifications, and equivalents. Numerous specific details are set forth in the following description in order to provide a thorough understanding of the invention. These details are provided for the purpose of example and the invention may be practiced according to the claims without some or all of these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the invention has not been described in detail so that the invention is not unnecessarily obscured.

Setup and Configuration

FIG. 1 is a diagram illustrating an embodiment of a System 100 for dynamically collecting, analyzing, and regulating garden parameters. Although the term “garden” is used throughout, it is to be understood that the systems and methods described herein are applicable to and can be used in any area or location where plants are grown or to be grown, including but not limited to orchards, farms, vineyards, lawns, nurseries, planters, fields, parks, etc. In addition, the systems and methods described herein are applicable to and can be used for other applications where embedded monitoring in a material may be useful, such as monitoring cement curing or water conditions near a dam, under road beds, in a levee, etc. As shown in FIG. 1, in some of the embodiments described herein, System 100 may comprise a Cloud Platform 110, which may communicate with a Router 120 via Network 130. In various embodiments, Network 130 may be the Internet, and can include wireless and wired WANs, LANs, or PANs. In some of the embodiments described herein, Router 120 may also communicate with Sensor 140 or Controller 150. In various embodiments, Router 120 may communicate with Sensor 140 or Controller 150 via a wireless connection. In various embodiments, Router 120 may communicate with Sensor 140 or Controller 150 using a Wi-Fi connection. It is to be understood that in various other embodiments of System 100 not shown in FIG. 1, Router 120 may communicate with any appropriate, required, or desired number (e.g., two or more) of sensors (e.g., Sensor 140) or controllers (e.g., Controller 150). It is also to be understood that Controller 150 is optional and in some embodiments of System 100 not shown in FIG. 1, System 100 may operate without Controller 150. Finally, it is to be understood that, in various embodiments, the functionalities of Sensor 140 and Controller 150 may be integrated into a single hybrid device or apparatus.

In various embodiments, Sensor 140 may be configured to take one or more measurements in order to generate Data 160. For example, in various embodiments, Sensor 140 may be capable of measuring temperature, light, and moisture. Meanwhile, in various embodiments, Data 160 may include at least one real time garden parameter, including but not limited to soil moisture, soil salinity, soil temperature, soil pH, external temperature, external humidity, and light exposure. In various embodiments, Sensor 140 may be configured to take measurements according to a predetermined or to a dynamic schedule or frequency. Furthermore, in various embodiments, Sensor 140 may also be configured to transmit Data 160 to Router 120. In some embodiments, Data 160 may be transmitted to Router 120 in real time. In other embodiments, Data 160 may be transmitted to Router 120 at preset intervals (e.g., hourly, daily, weekly) or at variable intervals, which can change dynamically based on one or more factors such as user input or the frequency of measurements taken by Sensor 140.

In some embodiments, Controller 150 may be configured to control sprinkler Valve 170. In various embodiments, Controller 150 may receive signals from Router 120 that include commands or instructions to open and to close Valve 170. In various embodiments, based on the instructions from Router 120, Controller 150 may then instruct Valve 170 to open or to close. In some embodiments, Valve 170 may be a solenoid valve, a type of valve that is commonly found in various residential and commercial sprinkler systems. In some embodiments, Controller 150 may be particularly well suited to controlling solenoid valves. In some embodiments, Controller 150 may directly cause Valve 170, a solenoid valve, to open and to close by modulating the electric current that controls the valve. Thus, in some embodiments, Controller 150 does not operate by regulating the water supply to Valve 170, but rather by modulating an electric current. Advantageously, in some embodiments, since Controller 150 may modulate the electric signals instead of the water flow to Valve 170, Controller 150 may be installed quickly and effortlessly without disrupting any of the existing pipelines connected to Valve 170. In some embodiments, Controller 150 may be installed as an addition to an existing, conventional sprinkler controller system. In various embodiments, Controller 150 and Valve 170 may be integrated into a single hybrid device or apparatus.

In various embodiments, Router 120 may be configured to communicate with Cloud Platform 110 through Network 130. In various embodiments, Router 120 may be configured to transmit data to Cloud Platform 110. As discussed earlier, in various embodiments, Router 120 may receive Data 160 (e.g., garden parameters) from Sensor 140. In various embodiments, Router 120 may then transmit Data 160 to Cloud Platform 110. In various embodiments, Router 120 may also be configured to receive Data 161 from Cloud Platform 110. As discussed earlier, Router 120 may transmit signals to Controller 150. In particular, Router 120 may transmit signals directing Controller 150 to cause Valve 170 to open and to close. In various embodiments, Router 120 may transmit to Controller 150 signals which it receives from Cloud Platform 110. In various embodiments, Cloud Platform 110 may analyze data, such as Data 160, in order to determine or to generate Data 161. For example, in some embodiments, Cloud Platform 110 may analyze various garden parameters (e.g., soil moisture, soil salinity, soil temperature, soil pH, external temperature, external humidity, light exposure) to determine whether or when to turn on (or off) Valve 170. In various embodiments, Data 161 may include data that is generated by applying one or more statistical analysis techniques to Data 160, including but not limited to extrapolating, interpolating, averaging, or segmenting Data 160. In some embodiments, Cloud Platform 110 may further receive Data 162. In various embodiments, Data 162 may come from any appropriate source or sources, and may include additionally relevant data such as the weather forecast. In some embodiments, Data 162 may include historical data and collective data (e.g., past or present garden parameters from multiple users). In some embodiments, Data 161 may include possible future garden parameters forecast based at least in part on Data 160 and Data 162. In various embodiments, Data 162 may also include user inputs, such as instructions, commands, and any other appropriate types of user feedback. For example, in various embodiments, users may be able to input and transmit to Cloud Platform 110 real life observations (e.g., moistness of the soil, health of plants) and, as will be discussed in more detail below, preferences (e.g., preferred plants, budgets). In various embodiments, Cloud Platform 110 may generate or determine Data 161 based on an analysis of one or both of Data 160 and Data 162.

In addition to Router 120, FIG. 1 further shows that in various embodiments, Cloud Platform 110 may also communicate with Device 181 and Device 182 via Network 130. In various embodiments, Devices 181 and 182 may be any appropriate type of computing and communication device, including but not limited to a smartphone, tablet PC, or personal computer. In various embodiments, Device 181 and Device 182 may each be capable of hosting Application 190. For example, in some embodiments, users may download and install Application 190 onto their respective devices of choice (e.g., Device 181, Device 182). In various embodiments, Application 190 may allow various users to communicate with Cloud Platform 110, including by sending, receiving, and otherwise interacting with various types of data. In particular, in some embodiments, Cloud Platform 110 may be capable of collecting and analyzing data, such as Data 160, from different gardens and over extended periods of time. In addition, in some embodiments, Cloud Platform 110 may keep track of the current status of one or both of Controller 150 and Valve 170. Thus, in various embodiments, users may use Application 190 to interact with (e.g., view) current, historical, and collective sets of data.

In various embodiments, users may also use Application 190 to see the current status of their respective sprinkler systems (e.g., whether Valve 170 is open or closed). In various embodiments, Cloud Platform 110 may transmit one or more alerts, notifications, or recommendations to the user via Application 190. For example, based on Data 160 (e.g., garden parameters measured by Sensor 140) and Data 162 (e.g., historical data and collective data from different users, weather, forecasted garden parameters, user inputs, etc.), Cloud Platform 110 may generate and transmit to the user an alert, notification, or recommendation that instructs the user to perform one or more actions in order to achieve certain garden parameters. In various embodiments, Cloud Platform 110 may recommend actions such as adding additives (e.g., fertilizers to change the pH or salinity in the soil), applying mulch (e.g., to control moisture level and soil temperature), and covering the plants (e.g., to encourage a more humid environment).

In some embodiments, Application 190 may provide one or more options for users to change or to override the current status of their respective sprinkler systems. That is, in some embodiments, a user may request, through Application 190, that Controller 150 force shuts Valve 170. However, as described earlier, it is to be understood that System 100 may, in various embodiments, also operate without Controller 150. In some embodiments, Controller 150 is optional equipment. In those embodiments where Controller 150 is not installed or is not in use, instead of sending commands or instructions to Controller 150 to turn on (and off) Valve 170, Cloud Platform 110 may transmit alerts, notifications, or recommendations to Device 181 or Device 182 instructing the user to manually turn on (and off) Valve 170.

Sprinkler systems are typically installed based on a multi-zone paradigm. That is, a planted area (e.g., garden, lawn, field, golf course) is divided into different zones and each sprinkler valve (or group of sprinkler valves) in the sprinkler system is designated to service a separate zone. Since each sprinkler valve (e.g., Valve 170) may require a separate controller (e.g., Controller 150) in the various embodiments of System 100 described with respect to FIG. 1, System 100 is ideally suited for a planted area with a relatively small number (e.g., two or fewer) zones. However, System 100 may nonetheless be used for a planted area with any number of zones.

FIG. 2 is a diagram illustrating an embodiment of a System 200 for dynamically collecting, analyzing, and regulating garden parameters. In various embodiments, System 200 is ideally suited for a planted area with a larger number of zones (e.g., more than two), though it may also be used for a planted area with fewer zones. As shown in FIG. 2, in some of the embodiments described herein, System 200 may comprise a Cloud Platform 210, which may communicate with a wireless Router 220 via Network 230. Meanwhile, according to FIG. 2, in some of the embodiments described herein, Router 220 may further communicate with a Hub 240. In various embodiments, Hub 240 may be Wi-Fi-enabled and may communicate with Router 220 via a Wi-Fi link. In various embodiments, Hub 240 may also communicate with Sensor 250. In various embodiments, Hub 240 and Sensor 250 may be capable of communicating over a low-power, sub-GHz link. In some embodiments, Hub 240 may communicate with Sensor 250 via a 900 MHz long distance radio link. In contrast to System 110, which was described with respect to FIG. 1, in various embodiments of System 200, Sensor 250 may not be required to communicate directly with Router 220 over a Wi-Fi link. Instead, in various embodiments of System 220, Hub 240 may act as a broker or intermediary that centralizes the communication of various types of data between Router 220 and Sensor 250. In the various embodiments of System 200, Sensor 250 may only be required to communicate data over a low-power sub-GHz link. Advantageously, in the various embodiments of System 200, Sensor 250 may be more power efficient than a Wi-Fi enabled counterpart, such as Sensor 140 described with respect to FIG. 1. It is to be understood that, although not shown in FIG. 2, in various other embodiments of System 200, Hub 240 may communicate with any appropriate, necessary, or desired number (e.g., two or more) of sensors (e.g., Sensor 250). It is also to be understood that Hub 240 is optional and in some embodiments of System 200 not shown in FIG. 2, System 200 operates without Controller 150.

In various embodiments, Sensor 250 may communicate with Hub 240 including by transmitting Data 260 to Hub 240. In various embodiments, Data 260 may be generated by Sensor 250 based on or using one or more measurements taken by Sensor 250 (e.g., temperature, humidity, light). In various embodiments, Data 260 may include at least one real time garden parameter, including but not limited to soil moisture, soil salinity, soil temperature, soil pH, external temperature, external humidity, and light exposure. In various embodiments, Sensor 250 may be configured to take measurements according to a predetermined or to a dynamic schedule or frequency, and to transmit Data 260 to Hub 240 in real time or at preset intervals (e.g., hourly, daily, weekly) or at variable intervals, which can change dynamically based on one or more factors such as user input or the frequency of measurements taken by Sensor 140.

In various embodiments, Hub 240 may be used instead of or in place of a conventional sprinkler control system because Hub 240 may be capable of controlling multiple sprinkler valves in a sprinkler system. This is in contrast to the various embodiments of System 100 wherein each valve (e.g., Valve 170) may require a separate controller (e.g., Controller 150). As FIG. 2 illustrates, Hub 240 may control the operations (e.g., opening and closing) of Valves 281, 282, and 283. However, it is to be understood that in various embodiments, Hub 240 may control any appropriate, required, or desired number of valves. In various embodiments, Hub 240 may be capable of opening (or closing) some or all of Valves 281, 282, and 283 including by determining and transmitting one or more control signals to the appropriate valve(s). In various embodiments, Hub 240 may determine which of Valves 281, 282, and 283 to open (or close) and when based at least in part on Data 261 from Router 220. As will be discussed in more detail below, in various embodiments, Data 261 may originate from Cloud Platform 210. In some embodiments, Data 261 may include explicit commands or instructions for Hub 240 to open (or close) some or all of Valves 281, 282, and 283. In some embodiments, Data 261 may include a fully or partially processed data set. Thus, in various embodiments, it is understood that Hub 240 may be capable of executing instructions (or commands) from Cloud Platform 210 to open (or close) some or all of Valves 281, 282, and 283. Instead or in addition, in some embodiments, Hub 240 may be capable of analyzing a fully or partially processed data set from Cloud Platform 210 in order to generate one or more instructions (or commands) to open (or close) some or all of Valves 281, 282, and 283.

Meanwhile, as discussed earlier, in various embodiments, Hub 240 may be capable of communicating with Cloud Platform 210 through wireless Router 220. In various embodiments, Hub 240 may be a Wi-Fi transceiver that transmits to Router 220 various types of data destined for Cloud Platform 210. For example, as discussed earlier, in some embodiments, Hub 240 may receive Data 260 from Sensor 250 over a sub-GHz (e.g., 900 MHz) link. Thus, in some embodiments, Hub 240 may transmit Data 260, whether immediately or according to a preset schedule or frequency, to Cloud Platform 210 through Router 220. In addition, in some embodiments, Hub 240 may be a Wi-Fi transceiver that receives from Router 220 various types of data, including Data 261, from Cloud Platform 210. In various embodiments, Cloud Platform 210, like Cloud Platform 110 discussed with respect to FIG. 1, may analyze data, such as Data 260, in order to determine or to generate Data 261. In various embodiments, Cloud Platform 210 may analyze various garden parameters (e.g., soil moisture, soil salinity, soil temperature, soil pH, external temperature, external humidity, and light exposure) to determine or to generate Data 261. In various embodiments, Data 261 may include data that generated by applying one or more statistical analysis techniques to Data 260, including but not limited to extrapolating, interpolating, averaging, and segmenting Data 260. In some embodiments, Cloud Platform 210 may further receive Data 262. In various embodiments, Data 262 may come from any appropriate source or sources, and can include additionally relevant data such as the weather forecast. In some embodiments, Data 262 may include historical data and collective data (e.g., past or present garden parameters from multiple users or gardens). As will be discussed in more detail below, Data 262 may also include user inputs, such as instructions, commands, and any type of appropriate user feedback. For example, in various embodiments, users may be able to input and transmit to Cloud Platform 210 real life observations (e.g., moistness of the soil, health of plants) and, as will be discussed in more detail below, preferences (e.g., preferred plants, budgets). In various embodiments, Cloud Platform 210 may generate or determine Data 261 based on an analysis of one or both of Data 260 and Data 262. In some embodiments, Data 261 may include possible future garden parameters forecasted based at least in part on Data 260 and Data 262. In some embodiments, Cloud Platform 210 may analyze one or both of Data 260 and Data 262 to determine whether and when to turn on (or off) some or all of Valves 281, 282, and 283. Thus, in some embodiments, Data 261 may include commands or instructions for Hub 240 to turn on (or off) some or all of Valves 281, 282, and 283. Alternately or in addition, in some embodiments, Cloud Platform 210 may fully or partially analyze one or both of Data 260 and Data 262 and then generate a refined, condensed, edited, or otherwise processed data set based on one or both of Data 260 and Data 261. As such, in some embodiments, Data 261 may include a fully or partially processed data set in addition to or in lieu of instructions (or commands) directed toward some or all of Valves 281, 282, and 283.

In addition to Router 220, FIG. 2 further shows that in various embodiments, Cloud Platform 210 may also communicate with Device 291 and Device 292 via Network 230. In various embodiments, Devices 291 and 292 may be any appropriate type of computing and communication device, including but not limited to a smartphone, tablet PC, or personal computer. In various embodiments, Device 291 and Device 292 may each be capable of hosting Application 293. For example, in some embodiments, users may download and install Application 293 onto their respective devices of choice (e.g., Device 291, Device 292). In various embodiments, Application 293 may allow various users to communicate with Cloud Platform 210, including by sending, receiving, and otherwise interacting with various types of data. In particular, in some embodiments, Cloud Platform 210 may be capable of collecting and analyzing data, such as Data 260, from different gardens and over extended periods of time. In addition, in some embodiments, Cloud Platform 210 may keep track, either directly or via Hub 240, of the current status of Valves 281, 282, and 283. Thus, in various embodiments, users may use Application 293 to interact with (e.g., view) current, historical, and collective sets of data.

In various embodiments, users may also use Application 293 to receive updates on the current status of their respective sprinkler systems (e.g., whether some or all of Valves 281, 282, and 283 are open or closed). In various embodiments, Cloud Platform 210 may also transmit one or more alerts, notifications, or recommendations to the user via Application 293. For example, based on Data 260 (e.g., garden parameters measured by Sensor 140) and Data 262 (e.g., historical data and collective data from different users, forecasted garden parameters, and user inputs), Cloud Platform 210 may generate and transmit to the user an alert, notification, or recommendation that instructs the user to perform one or more actions in order to achieve certain garden parameters. In various embodiments, Cloud Platform 210 may recommend actions such as adding additives (e.g., fertilizers to change the pH or salinity in the soil), applying mulch (e.g., to control moisture level and soil temperature), and covering the plants (e.g., to encourage a more humid environment).

In some embodiments, Application 293 may provide one or more options for users to change or to override the current status of their respective sprinkler systems. That is, in some embodiments, a user may request, through Application 190, that Hub 240 force shuts some or all of Valves 281, 282, and 283. However, as discussed earlier, it is to be understood that System 240 may, in various embodiments, also operate without Hub 240. In some embodiments, Hub 240 is optional equipment. In those embodiments where Hub 240 is not installed or is not in use, instead of sending commands or instructions to Hub 240 to turn on (and off) some or all of Valves 281, 282, and 283, Cloud Platform 210 may transmit alerts, notifications, or recommendations to Device 281 instructing the user to manually turn on (and off) some or all of Valves 281, 282, and 283.

As discussed earlier, in some embodiments of System 100 described with respect to FIG. 1, at least one sensor (e.g., Sensor 140) and one controller (e.g., Controller 150) may communicate directly with Router 120 via a wireless link (e.g., Wi-Fi). In some embodiments, it is to be understood that System 100 may include any appropriate, required, or desired number of sensors, controllers, and sensor-controller hybrids. As discussed earlier with respect to FIG. 2, in some embodiments of System 200, at least one sensor (e.g., Sensor 250), may communicate directly with a hub (e.g., Hub 240) over a wireless link (e.g., low power, sub-GHz link). As with System 100, in various embodiments of System 200, any appropriate, required, or desired number of sensors may be used. However, particularly in larger gardens, one or more devices may be out of range. For example, in some embodiments of System 100, one or more sensors, controllers, and sensor-controller hybrids may be unable to directly communicate with the router as a result of distance. Likewise, in some embodiments of System 200, one or more sensors may be outside of the hub's range. Under those circumstances, in some embodiments, two or more devices may form an ad-hoc or decentralized network. In some embodiments, data may be communicated amongst a group, sequence, or collection of devices until it reaches a device that is within range. For example, in some embodiments of System 100, one sensor, controller, or sensor-controller hybrid may communicate with one or more other sensors, controllers, or sensor-controllers over a wireless link (e.g., Wi-Fi, sub-GHz, ZigBee, Bluetooth) rather than directly with the router. In some embodiment of System 100, data may be passed along various sensors, controllers, or sensor-controller hybrids until it is within the range of the router. In a similar fashion, in some embodiments of System 200, one sensor may communicate with one or more other sensors over a wireless link (e.g., Wi-Fi, sub-GHz, ZigBee, Bluetooth) instead of with the hub, forming an ad-hoc or decentralized network such that data is forwarded from one sensor to another sensor until it is within the range of the hub. In some embodiments of System 100 or 200, an ad-hoc or decentralized network may be used as described above for other reasons, such as to minimize power consumption in transmitting data.

FIG. 3 is a flowchart illustrating an embodiment of a Process 300 for dynamically collecting, analyzing, and regulating garden parameters. In various embodiments, Process 300 may be performed by or at a cloud platform such as Cloud Platform 110 or Cloud Platform 210.

At 302, one or more garden parameters may be received. In various embodiments, the garden parameters received at the cloud platform may originate from one or more sensors (e.g., Sensor 140, Sensor 250) or sensor-controller hybrids. As discussed earlier, sensors (e.g., Sensor 140 and Sensor 250) as well as sensor-controller hybrids may be capable of measuring various garden parameters. In various embodiments, the garden parameters received at the cloud platform may originate from sensors that are installed in one or more different gardens. In various embodiments, garden parameters may include but are not limited to soil moisture, soil salinity, soil temperature, soil pH, external temperature, external humidity, and light exposure.

At 304, some or all of the garden parameters received at 302 may be analyzed. In various embodiments, one or more statistical analysis techniques may be applied. At 306, one or more garden parameters may be regulated based at least in part on the analysis of the received garden parameters. In various embodiments, regulating garden parameters may include changing or adjusting one or more current garden parameters in a garden. For example, in some embodiments, the cloud platform may analyze data including garden parameters from one or more gardens, the weather forecast, and user inputs to determine whether or not to turn on (or off) one or more sprinkler valves, and for how long. In some embodiments, turning on (or off) sprinkler valves for a certain period of time may cause desired or necessary changes in one or more garden parameters. For example, if one or more garden parameters (e.g., soil moisture, external humidity) indicate that the conditions in a garden are too arid, then the cloud platform may turn on one or more sprinkler valves for a certain amount of time in order to alleviate the aridity. Alternately or in addition, in some embodiments, the cloud platform may analyze data including garden parameters from one or more gardens, weather forecast, and user inputs to determine that the garden requires changes that entail human intervention, such as the addition of additives (e.g., fertilizer) and the application of mulch and other coverings. In some embodiments, the cloud platform may be configured to transmit an alert, notification, or recommendation to the user to perform the necessary actions.

Calibration and Optimization

As discussed with respect to FIG. 1 and FIG. 2, in various embodiments, the systems and methods described herein may utilize one or more sensors (e.g., Sensor 140, Sensor 250) or sensor-controller hybrids that are capable of measuring a variety of garden parameters. Examples of garden parameters include but are not limited to soil moisture, soil salinity, soil temperature, soil pH, external temperature, external humidity, and light exposure. In various embodiments, a sensor (or sensor-controller hybrid) may be configured to take measurements according to a predetermined or to a dynamically generated schedule or frequency. For example, in some embodiments, the sensor may be configured to take measurements at preset intervals (e.g., hourly, daily, weekly). In some embodiments, the sensor may be configured to take measurements at dynamically determined intervals. In particular, in some embodiments, the sensor may be configured to take measurements according to a dynamically generated schedule or frequency that is optimized based on specific garden conditions. For example, conditions in some gardens are volatile and change frequently while the conditions in other gardens remain relatively stable over time. Moreover, it is also often the case that some garden parameters fluctuate more frequently than others. Therefore, in some embodiments, the sensor may be configured to take the minimum number of measurements necessary to capture and provide an accurate and comprehensive range of data.

In some embodiments, a cloud platform (e.g., Cloud Platform 110, Cloud Platform 220) may receive data from the sensor that includes the various measurements taken by the sensor. In some embodiments, based on characteristics or patterns associated with the data, particularly over time, the cloud platform may be able to determine when or how frequently the sensor should optimally take its measurements. In some embodiments, data patterns or characteristics may include volatility and fluctuations that are best observed from a data set that is collected over a more extended period of time. In some embodiments, the cloud platform may develop a data profile that is specific to each garden. In some embodiments, the data profile for a garden may be built using data that includes past or historical garden parameters (i.e., different garden parameters that are collected over a period of time). Thus, in various embodiments, the cloud platform may be able to detect patterns or characteristics that indicate various levels of fluctuation or volatility based on the data profile for each garden. Thus, in some embodiments, based on data characteristic or patterns, the cloud platform may send commands or instructions to the sensor to take one or more measurements. In some embodiments, based on data characteristics or patterns, particularly with respect to individual garden parameters, the cloud platform may instruct or command the sensor to measure different garden parameters according to different schedules or frequencies. In various embodiments, the schedule or the frequency for the sensor to take measurements may be updated, adjusted, or refined as more data is collected over time and added to a garden's data profile.

As discussed earlier, sprinkler systems are typically set up into different zones. As such, in various embodiments, the sprinkler valve(s) (e.g., Valve 170, Valves 281-283) in each zone may be controlled separately based at least in part on the varying conditions in each zone. In various embodiments, regardless of the size of the garden and the number of zones, any appropriate, required, or desired number of sensors or sensor-controller hybrids may be deployed. For example, one user may install one sensor in each zone or multiple sensors in each zone. In particular, in some embodiments, one sensor can be shared across multiple zones (i.e., used in two or more zones). Advantageously, the portability and mobility of sensors and sensor-hybrids across different zones allow the various embodiments of the systems and the methods described herein to be implemented with a minimal amount of equipment, cost, and effort. Suppose, for example, that Alice wants to purchase and use only one sensor to measure garden parameters in two zones (e.g., Zone A, Zone B) in her backyard. In various embodiments, Alice may install an application (e.g., Application 190, Application 293) onto her smartphone or any other type of computing and communication device (e.g., personal computer, tablet PC). In various embodiments, using the application, Alice may input or otherwise indicate to the cloud platform the current zone that the sensor is in. Furthermore, in various embodiments, Alice may use the application to input or otherwise indicate to the cloud platform whenever the sensor is moved from one zone to another (e.g., from Zone A to Zone B, and vice versa). In those embodiments where a single sensor is used across multiple zones, the cloud platform may attribute garden parameters to the correct zone based on inputs or indications from the user. To continue the example, based on Alice's input or indication that the sensor is in Zone A, the cloud platform may attribute garden parameters received from the sensor to Zone A and not Zone B.

User Interactivity—Plant Recommendation

As discussed earlier, in various embodiments, users may communicate with the cloud platform (e.g., Cloud Platform 110, Cloud Platform 210) via the application (e.g., Application 190, Application 293), including by sending, receiving, and otherwise interacting with various types of data. Also as discussed earlier, in various embodiments, the cloud platform (e.g., Cloud Platform 110, Cloud Platform 210) may receive and analyze a variety of different types of data, including but not limited to data from sensors or sensor-controller hybrids (e.g., garden parameters from different users and gardens), weather forecast, user inputs, etc. In addition, the cloud platform may store at least a portion of the data to develop current and historical data profiles for a variety of different entities or groups of entities, including but not limited to different users, gardens, neighborhoods, regions, etc. In fact, in various embodiments, the cloud platform may be able to analyze, process, and store data in any appropriate manner, including but not limited to applying statistical analysis techniques such as aggregating, segmenting, averaging, interpolating, and extrapolating the data.

In various embodiments, the cloud platform may be able to recommend plant selections for a user. In some embodiments, the cloud platform may deliver its recommendations to the user via the application. In some embodiments, the cloud platform may determine an appropriate plant selection based on garden parameters from one or more sensors in the user's garden. For example, the appropriate plant selection may be determined based on current, historical, or predicted garden parameters from the user's own garden. Alternately or in addition, in some embodiments, the appropriate plant selection may be determined based on the type(s) of plants that have been successfully grown in a reference area, which includes but is not limited to identical, similar, or adjacent area(s). In various embodiments, based on criteria that include but are not limited to the availability or volume of data, the cloud platform may further determine an appropriately sized or situated reference area in order to determine an appropriate plant selection for a user's garden.

In some embodiments, in addition to garden parameters, the cloud platform may determine an appropriate plant selection further based on user preferences. In various embodiments, the user may stipulate his or her preferences at any level of specificity and based on any appropriate type of parameters (e.g., plant characteristics such as color, size, required maintenance). For example, Alice may indicate to the cloud platform (via the installed application) that she likes or prefers a specific type or species of peony (e.g., Chinese or Japanese peony). Alternately, Alice may indicate to the cloud platform that she likes or prefers plants that bear pink colored flowers. Based on the stated preference, the cloud platform may, in some embodiments, recommend plants that are appropriate for the user's garden. However, it can often be the case that a user indicates a preference for a plant or type of plant that is not appropriate given the user's garden parameters. Thus, in some embodiments, the cloud platform may recommend plant alternatives that are more ideal or better suited for the user's garden (i.e., plants that can survive or thrive based on the garden parameters from the user's garden) and which are similar to the preferred plant or type of plant. In some embodiments, the cloud platform's suggestions or recommendations may be displayed, via the application, in order of each plant's level of similarity to the preferred plant or type of plant. In some embodiments, the cloud platform's plant suggestions or recommendations may be displayed, in addition or instead, in order of the likelihood for each of the suggested plants to survive in the user's garden. In some embodiments, the cloud platform's plant suggestions or recommendations may be displayed with an option (e.g., a hyperlink) for the user to be directed to one or more e-commerce websites (e.g., Amazon.com®, Lowe's®, HomeDepot) that sells, carries, or can otherwise provide the plant(s).

In some embodiments, in addition to or instead of displaying plant suggestions or recommendations, the user may be given an option to enroll (via the application) in a plant subscription program (e.g., seed or plant of the month). In some embodiments, enrollment in the plant subscription program entitles the user to a regularly scheduled (e.g., monthly, quarterly) delivery of plants or seeds that may be curated from the cloud platform's plant suggestions or recommendations. In some embodiments, the cloud platform may determine and deliver (via the application) advertisements and links to other services (proprietary or third-party) that are appropriate for the user based on the user's garden parameters (e.g., soil quality) and inputs (e.g., preferred plants).

In some embodiments, the cloud platform may determine an appropriate plant selection based on the user's budget. In various embodiments, the cloud platform may take into consideration the user's budget in addition to or instead of the user's stated preferences. In various embodiments, the user's budget may include a one-time, initial setup cost. That is, the user may specify how much he or she can or would like to spend on plants and seeds for a garden. In various embodiments, the user's budget may further include a maintenance cost. For example, the user may specify, in addition to or instead, how much he or she can or would like to spend on maintaining the plants in the garden (e.g., water, fertilizer, mulch, etc.).

As discussed earlier, the cloud platform may use a variety of data (e.g., garden parameters, weather forecast, user inputs, etc.) to determine and to generate data indicating when the sprinkler system in a user's garden should be open (and closed). In some embodiments, the cloud platform may send commands or instructions to automatically turn on (and off) some or all of the valves in the sprinkler system. In some embodiments, the cloud platform may send instructions to the user to manually turn on (and off) some or all of the valves in the sprinkler system. In various embodiments, the cloud platform may collect and analyze a variety of data, including but not limited to current (e.g., the latest set of garden parameters), historical (e.g., garden parameters measured over a certain period time), and forecasted data (e.g., the weather forecast, extrapolated garden parameters). In various embodiments, the cloud platform may collect and analyze data from a variety of sources, including but not limited to data collected by sensors in the user's garden (e.g., Sensor 140, Sensor 250), data collected by sensors in other users' gardens, the weather forecast, and user feedback (e.g., real life observations of garden parameters and plant conditions). Thus, in various embodiments, the cloud platform may be capable of dynamically determining and regulating the conditions in a user's garden based on current, historical, and forecasted data. In various embodiments, a user may use the application to instruct the cloud platform to emulate or recreate the garden parameters in another user's garden. In an earlier example, Alice was unable to grow the same kind of peony that thrives in her neighbor Bob's yard. Suppose both Alice and Bob have installed System 100, as described with respect to FIG. 1, or System 200, as described with respect to FIG. 2. In this case, the cloud platform would have access to garden parameters from both Alice and Bob's gardens. The garden parameters from Bob's garden have proven to be conducive to cultivating peonies. As such, in some embodiments, Alice can instruct the cloud platform to regulate or modify her garden parameters, including by turning on and off one or more sprinkler valves and recommending one or more actions on the part of Alice (e.g., fertilizer, mulch, coverings), so that the garden parameters in Alice's garden match the garden parameters in Bob's garden.

In various embodiments, the cloud platform (e.g., Cloud Platform 110, Cloud Platform 210) may be configured to continuously interact with a user, a garden, and a microclimate (i.e., the regional environment and characteristics of the user's garden). In various embodiments, a control system may be paradigmatic of the interactions and the nexus between the cloud platform, the user, the garden, and the microclimate.

FIG. 4A is a diagram illustrating a Control System 400. As shown in FIG. 4A, in various embodiments, Control System 400 may comprise a Controller 410 and a Black Box 420. In various embodiments, Controller 410 may correspond to the system for dynamically collecting, analyzing, and regulating garden parameters (e.g., System 100, System 200) described herein. As described with respect to FIG. 1 and FIG. 2, various embodiments of the system for dynamically collecting, analyzing, and regulating garden parameters may include some combination of the following components: a cloud platform (e.g., Cloud Platform 110, Cloud Platform 220), sensors (e.g., Sensor 140, Sensor 250), user devices (e.g., Devices 181-182, Devices 291-292), and a hub (e.g., Hub 240) or valve controllers (e.g., Controller 150). Meanwhile, in various embodiments, Black Box 420 represents a typical garden ecosystem containing a user (e.g., Alice), a garden (e.g., Alice's garden), and a microclimate (e.g., Mission Valley in San Diego, Calif.). In various embodiments, Controller 410 may be configured to determine inputs (e.g., Inputs 430) into Black Box 420 based on the outputs (e.g., Outputs 440) from Black Box 420. In various embodiments, Inputs 430 from Controller 410 may include a variety of data that can be used to alter and is otherwise capable of altering conditions inside Black Box 420. In various embodiments, Outputs 440 may include a variety of data that is indicative of the conditions inside Black Box 420, including but not limited to readings, measurements, and feedback from the different constituents of Black Box 420 (e.g., the user, garden, and microclimate). In various embodiments, Controller 410 may continuously refine Inputs 430 based on Outputs 440 in order to determine a set of inputs that yields one or more ideal, desired, expected, or required outputs. In various embodiments, Controller 410 may determine a set of inputs that yields the ideal, desired, expected, or required outputs by satisfying requirements or conditions imposed by some or all of the constituents of Black Box 420. In various embodiments, Controller 410 may determine a set of inputs that would improve, influence, and otherwise change the current conditions in Black Box 420 (as indicated by a previous set of outputs) so that the next set of outputs from Black Box 420 would approximate or match the ideal, desired, expected, or required outputs.

For example, in some embodiments, users may have certain characteristics, which are typically manifest as one or more preferences for certain plants or for a particular budget. Meanwhile, in various embodiments, the user's garden may also exhibit a number of characteristics. Characteristics of the garden may be, in some embodiments, captured in the garden parameters measured by various sensors or in the user's observations. Finally, in some embodiments, the microclimate may host a variety of characteristics as well, including weather conditions such as sun, snow, rain, high winds, etc. Thus, in various embodiments, Outputs 440 may include readings and measurements of some or all of the characteristics of the constituents of Black Box 420. For example, in some embodiments, Outputs 440 may include but are not limited to user input, weather (e.g., current and forecasted), and various measured garden parameters. Based on Outputs 440, in various embodiments, Controller 410 may determine Inputs 430, which may include, as examples, commands and instructions to open (and close) sprinkler valves, and alerts, notifications, and recommendations to the user to perform one or more actions (e.g., water, fertilize, apply mulch, cover plant, etc.). As shown in FIG. 4A, in the various embodiments Control System 400, Controller 410, and Black Box 420 may be part of an iterative feedback loop. In various embodiments, Inputs 430 may both regulate and continuously evolve based on Outputs 440. In various embodiments, Outputs 440 may both change as a result of Inputs 430 and perpetuate the evolution of Inputs 430.

FIG. 4B is a flowchart illustrating an embodiment of a Process 401 for making plant recommendations. In various embodiments, Process 401 may be performed by or at a cloud platform (e.g., Cloud Platform 110, Cloud Platform 210). At 402, at least one plant selection criterion may be received. In various embodiments, plant selection criteria may originate from a number of different sources, including but not limited to sensors installed in the user's garden, and plant or budget preferences specified by the user.

At 404, at least one plant to recommend may be determined based at least in part on the selection criteria. For example, in some embodiments, the cloud platform may determine one or more plants to recommend based on the current, historical, or forecasted garden parameters in the user's garden. Alternately or in addition, in some embodiments, the cloud platform may determine one or more plants to recommend based on preferences stated by the user, including but not limited to the user's budget and the user's preferred plant selections. Furthermore, in some embodiments, in scenarios where the user's preferred plant selections are inappropriate or unsuited for the user's garden (e.g., based on the user's current, historical, or forecasted garden parameters), the cloud platform may be able to recommend plants that are similar to the user's preferred plants but can also tolerate or thrive in the user's garden.

User Interface

In the various embodiments of the systems and methods described herein, various garden events may be converted into one or more appropriate units of accounting. In various embodiments, examples of garden events may include but are not limited to turning on the sprinkler system, rainfall, fertilizing, and applying mulch and other coverings. In particular, in some embodiments, garden events may be converted into an appropriate currency (e.g., USD, Euros) in order to reflect the expenditures and savings generated by each garden event. As one example, in various embodiments, the cloud platform (e.g., Cloud Platform 110, Cloud Platform 210) may keep track of the current status of sprinkler valves (e.g., Valve 170, Valves 281-283). Thus, in various embodiments, the cloud platform may determine when each sprinkler system was turned on and for how long. Consequently, in various embodiments, the cloud platform may estimate the amount of water that was used and convert this information into a dollar amount (e.g., it costs $5 to water Zone A for 30 minutes).

In another example, the cloud platform may be configured to send an alert, notification, or recommendation that instructs a user to perform one or more actions. In some embodiments, the alert, notification, or recommendation may further include an estimated cost associated with the instructed actions. For instance, the cloud platform may determine based on a variety of data including a difference between the current pH or salinity level of the soil, and the ideal, desired, required, or expected level(s), that a certain amount of fertilizer is needed. In some embodiments, the cloud platform's alert, notification, or recommendation instructing the user to apply fertilizer to the user's garden may include the cost for the amount of fertilizer needed.

In various embodiments, the cloud platform may also estimate the amount of savings generated by a garden event. For example, if it rained profusely one day, based on data such as the weather forecast and the level of moisture in the soil after the rain, the cloud platform may determine and indicate to the user that his or her garden will not have to be watered for at least 8 days, generating a savings of $20. In various embodiments, the cloud platform may further estimate the cost of recreating ideal garden parameters or the specific garden parameters found in another garden. In an earlier example, Alice wishes to recreate the garden parameters in Bob's garden. The cloud platform may, in various embodiments, approximate the overall or individual cost corresponding to the amount of water, fertilizer, and mulch that are required to transform the existing conditions in Alice's garden so that the two gardens would exhibit identical or near-identical garden parameters.

In various embodiments, the cloud platform may analyze various types of data and generate different metrics indicating the efficiency of a user's sprinkler system. As discussed earlier, in various embodiments, the cloud platform may be able to estimate the amount of water usage based on information regarding when the user's sprinkler system was in use. In various embodiments, the cloud platform may assess the amount of water usage relative to one or more different benchmarks, including but not limited to an ideal, desired, required, and expected set of garden parameters. Suppose, for example, that Alice's sprinkler system was turned on for a certain period time (e.g., X minutes). In some embodiments, the cloud platform may estimate what the expected level of moisture in Alice's garden should be after X minutes of watering. In some embodiments, the cloud platform's estimate may be generated based on a host of different types of data (e.g., Data 161-162, Data 261-262), including but not limited to current weather conditions (e.g., temperature, humidity, wind), historical garden parameters measurements from both Alice and other similar gardens taken before and after watering, and empirical statistics. In various embodiments, the cloud platform may determine the efficiency of Alice's sprinkler system as a measure of how close (e.g., expressed as a percentage) the moisture level in Alice's garden is to the expected moisture level. Alternately, returning again to the previous example where Alice is attempting to recreate the garden parameters in Bob's garden, in some embodiments, the cloud platform may also determine the efficiency of Alice's sprinkler system in terms of the proximity between the moisture level in her garden after X minutes of watering and the ideal moisture level found in Bob's garden.

In various embodiments, the cloud platform may also take into account the cost of utilities, such as via electric or water rates, to determine when it is most cost-efficient to operate a sprinkler system. In some embodiments, a user may be able to select a cost-efficient profile, which may cause the cloud platform to adjust how it regulates the one or more garden parameters or determines plant recommendations based on the cost of utilities. In various embodiments, the cloud platform may estimate the amount of resources from a utility that would be used for an event and convert this information into a dollar amount (e.g., it costs $5 to water Zone A for 30 minutes at 6 AM, it costs $10 to water Zone A for 30 minutes at 2 PM). In various embodiments, the cloud platform may also take into account legal restrictions, such as water restrictions, to determine when operating a sprinkler system is permissible. In such embodiments, the cloud platform may regulate the one or more garden parameters to avoid operating a sprinkler system during impermissible times or notify a user that a legal restriction may apply to their garden for a watering time selected by a user.

FIG. 5 is a block diagram illustrating an embodiment of a wired or wireless System 550 that may be used in connection with the various embodiments described herein. For example, System 550 may be used to implement Devices 181-182 as described with respect to FIG. 1, as well as Devices 291-292 and Hub 240, as described with respect to FIG. 2. In some embodiments, System 550 may be a conventional personal computer, computer server, personal digital assistant, smart phone, tablet computer, or any other processor-enabled device that is capable of wired or wireless data communication. However, it is to be understood that other computer systems or architectures may be also used, as will be clear to those skilled in the art.

System 550 preferably includes one or more processors, such as Processor 560. Additional processors may be provided, such as an auxiliary processor to manage input/output, an auxiliary processor to perform floating point mathematical operations, a special-purpose microprocessor having an architecture suitable for fast execution of signal processing algorithms (e.g., a digital signal processor), a slave processor subordinate to the main processing system (e.g., a back-end processor), an additional microprocessor or controller for dual or multiple processor systems, or a coprocessor. Such auxiliary processors may be discrete processors or may be integrated with Processor 560.

Processor 560 may preferably be connected to a Communication Bus 555. Communication Bus 555 may include a data channel for facilitating information transfer between storage and other peripheral components of System 550. Communication Bus 555 may further provide a set of signals used for communication with Processor 560, including a data bus, address bus, and control bus (not shown). Communication Bus 555 may comprise any standard or non-standard bus architecture such as, for example, bus architectures compliant with industry standard architecture (“ISA”), extended industry standard architecture (“EISA”), Micro Channel Architecture (“MCA”), peripheral component interconnect (“PCI”) local bus, or standards promulgated by the Institute of Electrical and Electronics Engineers (“IEEE”) including IEEE 488 general-purpose interface bus (“GPIB”), IEEE 696/S-100, etc.

System 550 preferably includes a Main Memory 565 and may also include a Secondary Memory 570. Main Memory 565 provides storage of instructions and data for programs executing on Processor 560. Main Memory 565 is typically semiconductor-based memory such as dynamic random access memory (“DRAM”) or static random access memory (“SRAM”). Other semiconductor-based memory types include, for example, synchronous dynamic random access memory (“SDRAM”), Rambus dynamic random access memory (“RDRAM”), ferroelectric random access memory (“FRAM”), etc., including read only memory (“ROM”).

Secondary Memory 570 may optionally include an internal memory (Internal Medium 575) or a Removable Medium 580, for example a floppy disk drive, a magnetic tape drive, a compact disc (“CD”) drive, a digital versatile disc (“DVD”) drive, etc. Removable Medium 580 is read from or written to in a well-known manner. Removable Medium 580 may be, for example, a floppy disk, magnetic tape, CD, DVD, SD card, etc.

Removable Medium 580 is a non-transitory computer readable medium having stored thereon computer executable code (i.e., software) or data. The computer software or data stored on Removable Medium 580 is read into System 550 for execution by Processor 560.

In alternative embodiments, Secondary Memory 570 may include other similar methods of allowing computer programs or other data or instructions to be loaded into System 550. Such methods may include, for example, an external storage medium (External Medium 595) and a Communication Interface 570. Examples of External Medium 595 may include an external hard disk drive or an external optical drive, or an external magneto-optical drive.

Other examples of Secondary Memory 570 may include semiconductor-based memory such as programmable read-only memory (“PROM”), erasable programmable read-only memory (“EPROM”), electrically erasable read-only memory (“EEPROM”), or flash memory (block oriented memory similar to EEPROM). Also included are any other removable storage media (Removable Medium 580) and Communication Interface 590, which allow software and data to be transferred from External Medium 595 to System 550.

System 550 may also include an input/output (“I/O”) interface, I/O Interface 585. I/O Interface 585 facilitates input from and output to external devices. For example I/O Interface 585 may receive input from a keyboard or mouse and may provide output to a display. I/O Interface 585 is capable of facilitating input from and output to various alternative types of human interface and machine interface devices.

System 550 may also include a Communication Interface 590. Communication Interface 590 allows software and data to be transferred between System 550 and external devices (e.g. printers), networks, or information sources. For example, computer software or executable code may be transferred to System 550 from a network server via Communication Interface 590. Examples of Communication Interface 590 include a modem, a network interface card (“NIC”), a wireless data card, a communications port, a PCMCIA slot and card, an infrared interface, and an IEEE 1394 fire-wire.

Communication Interface 590 preferably implements industry promulgated protocol standards, such as Ethernet IEEE 802 standards, Fiber Channel, digital subscriber line (“DSL”), asynchronous digital subscriber line (“ADSL”), frame relay, asynchronous transfer mode (“ATM”), integrated digital services network (“ISDN”), personal communications services (“PCS”), transmission control protocol/Internet protocol (“TCP/IP”), serial line Internet protocol/point to point protocol (“SLIP/PPP”), and so on, but may also implement customized or non-standard interface protocols as well.

Software and data transferred via Communication Interface 590 are generally in the form of electrical communication signals 605. These signals 605 are preferably provided to Communication Interface 590 via a communication channel 600. In one embodiment, the communication channel 600 may be a wired or wireless network, or any variety of other communication links. Communication channel 600 carries signals 605 and may be implemented using a variety of wired or wireless communication means including wire or cable, fiber optics, conventional phone line, cellular phone link, wireless data communication link, radio frequency (“RF”) link, or infrared link.

Computer executable code (i.e., computer programs or software) may be stored in Main Memory 565 or Secondary Memory 570. Computer programs may also be received via Communication Interface 590 and stored in Main Memory 565 or Secondary Memory 570. Such computer programs, when executed, enable System 550 to perform the various functions as previously described.

In this description, the term “computer readable medium” is used to refer to any non-transitory computer readable storage media used to provide computer executable code (e.g., software and computer programs) to System 550. Examples of these media include Main Memory 565, Secondary Memory 570 (including Internal Medium 575, Removable Medium 580, and External Medium 595), and any peripheral device communicatively coupled with Communication Interface 590 (including a network information server or other network device). These non-transitory computer readable mediums are used to provide executable code, programming instructions, and software to System 550.

In an embodiment that is implemented using software, the software may be stored on a computer readable medium and loaded into System 550 by way of Removable Medium 580, I/O Interface 585, or Communication Interface 590. In such an embodiment, the software is loaded into System 550 in the form of electrical communication signals 605. The software, when executed by Processor 560, preferably causes Processor 560 to perform the inventive features and functions previously described herein.

System 550 may also include optional wireless communication components that may facilitate wireless communication over a voice or data network. The wireless communication components may comprise an antenna system (Antenna 610), a radio system (Radio 615) and a baseband system (Baseband 620). In System 550, radio frequency (“RF”) signals may be transmitted or received over the air by Antenna 610 under the management of Radio 615.

In one embodiment, Antenna 610 may comprise one or more antennae and one or more multiplexors (not shown) that perform a switching function to provide Antenna 610 with transmit and receive signal paths. In the receive path, received RF signals may be coupled from a multiplexor to a low noise amplifier (not shown) that amplifies the received RF signal and sends the amplified signal to Radio 615.

In alternative embodiments, Radio 615 may comprise one or more radios that are configured to communicate over various frequencies. In one embodiment, Radio 615 may combine a demodulator (not shown) and modulator (not shown) in a single integrated circuit (“IC”). The demodulator and modulator may also alternatively be separate components. In the incoming path, the demodulator strips away the RF carrier signal leaving a baseband receive audio signal, which is sent from Radio 615 to Baseband 620.

If the received signal contains audio information, Baseband 620 decodes the signal and converts it to an analog signal. Then the signal is amplified and sent to a speaker. Baseband 620 also receives analog audio signals from a microphone. These analog audio signals are converted to digital signals and encoded by Baseband 620. Baseband 620 also codes the digital signals for transmission and generates a baseband transmit audio signal that is routed to the modulator portion of Radio 615. The modulator mixes the baseband transmit audio signal with an RF carrier signal generating an RF transmit signal that is routed to the antenna system and may pass through a power amplifier (not shown). The power amplifier amplifies the RF transmit signal and routes it to Antenna 610 where the signal is switched to the antenna port for transmission.

Baseband 620 is also communicatively coupled with Processor 560. The central processing unit, Processor 560, has access to the data storage areas, Main Memory 565 and Secondary Memory 570. The central processing unit, Processor 560, is preferably configured to execute instructions (i.e., computer programs or software) that can be stored in Main Memory 565 or Secondary Memory 570. Computer programs can also be received from the baseband processor, Baseband 610, and stored in Main Memory 565 or in Secondary Memory 570, or executed upon receipt. Such computer programs, when executed, enable System 550 to perform the various functions as previously described. For example, Main Memory 565 may include various software modules (not shown) that are executable by Processor 560.

FIG. 6 is a block diagram illustrating an embodiment of a wireless system 640 that may be used in connection with the various embodiments described herein. For example System 640 may be used to implement Sensor 140 as described with respect to FIG. 1, as well as Sensor 250, as described with respect to FIG. 2. In some embodiments, System 640 may comprise a Microcontroller 650, which may offer integrated functions such as an analog-to-digital converter ADC 655, a digital-to-analog converter DAC 660, an Inter-Integrated Circuit interface I2C 665, a Human-Machine Interface HMI 670, and a transceiver Tx/Rx 675. In various embodiments, Microcontroller 650 may be implemented using a microcontroller manufactured by Freescale Semiconductor, Inc., such as the Freescale Kinetis KW01.

In some embodiments, System 640 may also include one or more sensors, such as Sensor 680, Sensor 685, Sensor 690, and Sensor 695. For example, Sensor 680 may output a voltage based on a measurement of temperature, pH, or light. In some embodiments, ADC 655 may measure the voltage supplied by Sensor 680, after which Microcontroller 650 may determine the soil temperature, soil pH, or ambient light based on the voltage measurement. As another example, Sensor 685 may be comprised of a PCB 700 as shown in FIG. 7, wherein PCB 700 may be composed of interleaved traces on the PCB. The first set of traces may be connected to DAC 660 to receive a specified voltage and a second set of traces, which are not connected to the first set of traces as shown by the rounded hop-overs, may be connected to ADC 655 and a resistor of known value connected to ground. Based on the voltage applied by DAC 660 and the voltage measured by ADC 550, Microcontroller 650 may determine a soil resistance based on the voltage difference between the applied and measured voltages. In some embodiments, the applied voltage may be stepped across a range of voltages, thereby extending the dynamic range of ADC 655.

As another example, Sensor 690 may also use an off-the-shelf temperature or humidity measurement component that can communicate with Microcontroller 650 via I2C 665.

As another example, a touch sensor is designed to accurately read small capacitive changes induced by skin contact with a capacitive touch screen. HMI 670 may also use such a touch sensor, except HMI 670 may be connected to Sensor 695 that contains capacitive plates in contact with soil rather than a touch screen. By measuring the capacitance of such plates in Sensor 695 at different frequencies via HMI 670, Microcontroller 650 may determine a soil's dielectric constant. In various embodiments, Microcontroller 650 may further determine the soil's moisture content using the soil's dielectric constant, salinity, and methods known in the art (e.g., Topp et al., “Electromagnetic determination of soil water content: Measurements in coaxial transmission lines,” Water Resources Research, June 1980).

In various embodiments, System 640 may use well-known methods in the art to support multiple sensors as described above, such as by using multiplexers/demultiplexers. Accordingly, in various embodiments, numerous sensors for different measurements may be connected to ADC 655, DAC 660, I2C 665, or HMI 670 and the use of ADC 655, DAC 660, I2C 665, or HMI 670 for obtaining measurements from one sensor does not preclude ADC 655, DAC 660, I2C 665, or HMI 670 from being used for obtaining measurements from another sensor. By using such methods well known in the art, System 640 may determine a parameterized measurement of a soil's salinity, pH, and evaporation rates based on measurements of a soil's resistance, capacitance, pH, and temperature as well as other measurements such as measurements of ambient light, air temperature, and air humidity.

In various embodiments, System 640 may transmit the measurements from various sensors as described above to a cloud platform (e.g., Cloud Platform 110, Cloud Platform 210). The cloud platform may perform the same analysis, calibration, or other adjustments to the measurements as described herein with respect to System 640. In some embodiments, an intermediate component, such as Hub 240, Device 181, Device, 182, Device 291, or Device 292, may also perform analysis, calibration, or other adjustments based on the measurements described herein with respect to System 640. By using such methods well known in the art, the cloud platform or intermediate components (e.g., Hub 240, Device 181, Device, 182, Device 291, or Device 292) may determine a parameterized measurement of a soil's salinity, pH, and evaporation rates based on measurements of a soil's resistance, capacitance, pH, and temperature as well as other measurements such as measurements of ambient light, air temperature, and air humidity. In various embodiments, the cloud platform or intermediate components (e.g., Hub 240, Device 181, Device, 182, Device 291, or Device 292) may then transmit results of the above analysis to System 640 or other components described herein. For example, System 640 may receive instructions to adjust future measurements based on analysis, calibration, or other adjustments from the cloud platform or intermediate components (e.g., Hub 240, Device 181, Device, 182, Device 291, or Device 292).

In some embodiments, Microcontroller 650 may also include TX/RX 675. In such embodiments, TX/RX 675 may be an 802.15.4-compliant device that has the ability to adjust its data rates and transmitter power using measurements of received signal strength from other transmitting devices. In various embodiments, TX/RX 675 may use other wireless methods such as ZigBee, Bluetooth, or Wi-Fi.

FIG. 8 is a diagram illustrating an embodiment of a System 800 for collecting garden parameters, which is an improvement on the system disclosed in U.S. Pat. No. 5,479,106 to Campbell, the entirety of which is incorporated herein. As shown in FIG. 8, in some of the embodiments described herein, System 800 may comprise a Sensor Housing 802, which may contain a System Component 804 and an RF Component 806. Further, Sensor Housing 802 may provide a physical and electrical interface between Electrical Conducting Structure 808 and System Component 804. Sensor Housing 802 may shield the sensitive electronics of System Component 804 and RF Component 806 from water, other contaminants, and mechanical or electrical damage. In some embodiments, the interior of Sensor Housing 802 may be filled with a solid or gelatinous compound to provide resistance to shock and vibration or for the exclusion of moisture or other corrosive agents.

System Component 804 may contain electronics that may be used to make basic electrical measurements for determining soil properties or to transmit data to a data collection platform. In some embodiments, System Component 804 may also contain electronics that may be used to receive data from a data collection platform. System Component 804 may be coupled to Electrical Conducting Structure 808. Electrical Conducting Structure 808 may form a capacitive element when placed in soil, thereby allowing System Component 804 to measure the soil electrical characteristics via Electrical Conducting Structure 808. In some embodiments, Electrical Conducting Structure 808 may be a capacitive touch sensor. In other embodiments, Electrical Conducting Structure 808 may be two or more metal strips slightly separated from each other.

RF Component 806 may contain internal batteries, a radio, and an antenna for transmission and reception of data between System Component 804 and a data collection platform. In some embodiments, RF Component 806 may use a multi-conductor wire to power itself and System Component 804 instead of using internal batteries.

FIG. 9 is a diagram illustrating an embodiment of a System 900 for performing sensor measurements within System Component 804. As shown in FIG. 8, Oscillator 902 may be coupled to a Bandpass Filter 904. Oscillator 902 may be used to apply a high frequency electrical signal (e.g., 10 to 200 MHz) to the Bandpass Filter 904. In some embodiments, the oscillator may be a simple square-wave clock oscillator and Bandpass Filter 904 may then be used to eliminate the higher order harmonic frequencies to form a sinusoidal signal. In other embodiments, Oscillator 902 may be a different type of oscillator (e.g., sine-wave) and Bandpass Filter 904 may be used to eliminate any undesirable frequency components or omitted. The output of Bandpass Filter 904 may then be applied to a capacitor C1, which may act as a blocking capacitor to filter out any undesirable DC component in the signal output of Bandpass Filter 904.

In some embodiments, a bias voltage may be applied at Vbias across R1 (shown in FIG. 9) for facilitating sensitive analog measurements. The output of R1 and C1 may then be combined and coupled to the input of AC Meter 906 and the input of R2. The output of R2 may then be coupled to the input of AC Meter 908 and the input of R3. The output of R3 may then be coupled to the input of AC Meter 910 and capacitor C2. In such embodiments, AC Meters 906, 908, and 910 along with resistors R2 and R3 may be understood to form a sensitive bridge circuitry that may facilitate measuring differences, as explained below, in the amplitude of an AC electrical signal originally generated by Oscillator 902. Capacitor C2 may also act as a blocking capacitor to prevent any DC component from the bias voltage passing to Electrical Conducting Structure 808. This may be necessary where such a DC component may cause galvanic corrosion of the conducting structure in the presence of the soil or where such a bias voltage would distort measurements if applied to Electrical Conducting Structure 808. Capacitor C2 may then be connected to Electrical Conducting Structure 808, which may be represented as a resistor Rs and capacitor Cs in parallel, so that the AC signal is applied to Electrical Conducting Structure 808.

In various embodiments, DC meters may be used instead of AC Meters 906, 908, and 910, such as where the cost of accurate AC Meters would be too high. FIGS. 10A and 10B show diagrams of two such approaches for using DC meters instead of AC meters to measure AC signals.

With respect to FIG. 10A, System 1000 is shown for measuring AC signals, which may use a DC Meter 1002, a diode D1, a resistor Rd, and a capacitor Cd. In such an embodiment, if an AC signal is applied to diode D1, only the positive component of the AC signal may pass through diode D1. This filtered AC signal may then be applied to the RC circuit that uses Rd and Cd to create a filtered DC signal that may be measured by DC Meter 1002. In such an embodiment, the amplitude of the DC signal may correspond to the amplitude of the AC signal that was applied to the input of diode D1.

With respect to FIG. 10B, an alternative System 1050 is shown for measuring AC signals, which may use a DC Meter 1052, a transistor T1, a resistor Rd, and a capacitor Cd. In such an embodiment, if an AC signal is applied to transistor T1, only the positive component of the AC signal may pass through transistor T1. This filtered AC signal may then be applied to the RC circuit that uses Rd and Cd to create a filtered DC signal that may be measured by DC Meter 1052. In such an embodiment, the amplitude of the DC signal may correspond to the amplitude of the AC signal that was applied to the input of transistor T1.

In such embodiments as shown in FIGS. 10A and 10B, a bias voltage may need to be combined with the AC signal to facilitate the accurate measurement of the AC signal. For example, biasing the AC signal may dramatically improve the linearity of the signal applied to the DC meters or allow for much smaller AC signal levels to be measured.

With respect to FIG. 11, a Method 1100 is shown for measuring soil characteristics. At step 1110, Oscillator 902 may be turned off. At step 1120, measurements of voltages Voff1, Voff2, and Voff3 may be respectively recorded by AC Meters 906, 908, and 910. At step 1130, Oscillator 902 may be turned on. At step 1140, measurements of voltages Von1, Von2, and Von3 may be respectively recorded by AC Meters 906, 908, and 910. At step 1150, the values of A and B may be computed as follows: A=(Von2−Voff2)/(Von1−Voff1) and B=(Von3−Voff3)/(Von1−Voff1). At step 1160, Cs and Rs may then be computed based on the known values of R2, R3, A, and B, such as by the method shown in U.S. Pat. No. 5,479,106. At step 1170, soil moisture level and soil salinity may be determined based on Rs and Cs using methods known in the art.

Taking measurements of voltages with the improved bridge circuit shown in FIG. 8 using AC Meters 906, 908, and 910 when the oscillator is both on and off as described by Method 1100 may reduce measurements problems relating to temperature dependence in the meter output, avoid the need for taking into account varying diode forward voltage drop when calculating values A and B in step 1150, and allow for lower cost components to be used as compared with techniques known in the art.

Hostile radio environments (lossy media, widely varying media, etc.), such as soils, cement, human or animal tissue, building materials, water including seawater, and other media, often involve short transmission ranges. Such media may pose a distinct challenge to traditional RF telemetry in that they may strongly attenuate RF energy, and thus dramatically reduce communication range as well as making transmit/receive antenna design difficult. In addition, in media with widely varying electrical properties such as soil, an antenna that is an efficient radiator under one condition, may be a very poor radiator if the soil moisture or salinity changes. One embodiment for resolving these problems may operate using “near field” principles by implementing an oscillating electrical stimulus at a frequency such that the electromagnetic wave in the medium is much larger than the transmitting and receiver structure sizes as well as the separation between them.

With respect to FIG. 12, an application of such a System 1200 for soil monitoring is shown. System 1200 may use a Receiver 1202 connected by cable 1206 to Transmit/Receive Structure 1208. As shown in the example, Receiver 1202 may be elevated, such as by use of Pole 1204, to protect the electronics from environmental factors or other concerns. System 1200 may also use one or more Sensors (e.g., 1210, 1214, 1218) that may each be respectively coupled to a Transmit/Receive Structure (e.g., 1212, 1216, 1220).

In some embodiments, Receiver 1202 may be located (typically at distances less than 5 m or so) near Sensors 1212, 1216, and 1220. In such an arrangement, if Sensors 1212, 1216, and 1220 are transmitting, then an electrical signal may be produced in Receiver 1202's receiver structure which may then be demodulated to recover the Sensor data transmission. Receiver 1202 may use this same receiver structure, or a separate transmit structure, to communicate with the Sensor. In various embodiments, the arrangement may include additional receivers and sensors distributed over a geographic area.

Receiver 1202 may typically include a power source, such as batteries, solar power, a utility connection, or some other power source. The Receiver may be fixed in place or mounted onto mobile equipment such as drones, equipment such as tractors, combines, center pivots, or other devices that move over an area that is to be monitored. In addition, Receiver 1202 may have memory to store data as well as an additional radio, such as a cellular modem, a 934 MHz telemetry radio, a direct connection to the Internet through a Wi-Fi router or Ethernet, or other means of communicating collected sensor data to a network. By including such an additional radio, various embodiments may allow for remote monitoring of collected data and system operating parameters, as well as potentially reconfiguring the system's monitoring performance or software.

Sensors 1210, 1214, and 1218 may be buried in the soil and may each be equipped with a Transmit/Receive Structure (e.g., 1212, 1216, and 1220). Transmit/Receive Structure 1212, 1216, or 1220 may use a coil of one or more turns that may be electrically insulated from the soil, which may be tuned with additional capacitors to form a resonant structure at a desired drive frequency. In addition, such coils may incorporate a ferrite or other material of high magnetic permeability to enhance a generated magnetic field. Such wire coils may be driven with an electrical current at a desired frequency with a suitable modulation scheme so as to encode a sensor identification code, sensor operating parameters such as battery level, and any data measured by Sensor 1210, 1214, or 1218. If such Sensors are placed in soil, such data may include parameters such as soil moisture, salinity, and temperature. In addition, Sensors 1210, 1214, or 1218 may use the same transmitting structure, or a separate structure, to receive information, trigger measurement, update sensor firmware, change operating parameters, or facilitate sensor location by allowing the Sensor to act as a transponder. Sensors 1210, 1214, or 1218 may each use a power source such as a battery, a microcontroller, circuitry used to make one or more desired measurements, and circuitry to drive and modulate/demodulate the transmitting/receiver structure.

In some embodiments, a Data Analytic Component 1250 may be included in System 1200. Data Analytic Component 1250 may reside on a server or at one or more the receivers of System 1200. Data Analytic Component 1250 may process incoming sensor data to provide management decisions and possibly implement control operations such as turning on irrigation, managing irrigation efficiently with respect to peak electrical power costs, scheduling agricultural operations such as spraying, tilling, etc. to reflect the actual conditions. In addition, Data Analytic Component 1250 may adapt System 1200 based on incoming data to reconfigure sensing to acquire enhanced information about the area being monitored.

For example, in the case of agriculture, water infiltration rates provide key information as to the most efficient irrigation strategies, plant health, and whether cultural practices such as deep tilling are indicated. A fixed interval soil moisture sensing approach may be optimal for battery life management in routine monitoring, but may be otherwise insufficient to capture the dynamic changes in soil moisture that occur when irrigation is in progress. To capture these events and better characterize the site, the system may increase the frequency of the Sensor soil moisture measurement and data transmission to the Receiver. This enhanced frequency data can then be used to derive better estimates of water infiltration rates.

The communication link between a Receiver and a Sensor in System 1200 may depend on near field interactions between the transmitting and receiving structures. In one implementation, these structures may be wire loops including one or more turns of insulated wire. They may also incorporate tuning inductors/capacitors to form a resonant structure at the intended operating frequency, which has the advantage of decreasing the required drive level in the transmitter, increasing the receiver sensitivity at the operating frequency, and suppressing noise at other frequencies. In addition, the coils may incorporate ferrites or other high permeability materials to enhance the transmitted and received signals. Other possible implementations include simple electric dipole antennas that use linear meandering wires, but magnetic wire loops have significant advantages.

An oscillating electrical current in the transmitting coil may produce an oscillating magnetic field that intersects with the receiving coil. This may then produce an oscillating current in the receiving coil through induction that may then be detected by a Receiver in System 1200. As such, if a transmitting coil current is modulated, a receiving coil may have a similarly modulated current. In such a situation, by using a suitable modulation scheme (e.g., amplitude modulation) may allow the Receiver to recover Sensor data. The process may also be reversed to allow a Receiver in System 1200 to communicate with a Sensor in System 1200.

With respect to FIG. 13, an example of a wire coil connected to a tuning capacitor is shown that may be used as a transmitting or receiving structure in System 1200. The drive frequency when used as a transmitting loop may be varied from a few kHz up to a few MHz with the only restriction being that the transmit and receive coils have a total length for each coil that is much smaller than a quarter wavelength of the drive frequency. As such, at 3 MHz total coil lengths can be as much as 10 m, while at 100 kHz, they can be very long-up to a few hundred meters.

While range may be much more limited in System 1200 than when compared with traditional RF telemetry approaches, the shorter range and very low fields in the far field may allow for unlicensed FCC operation by providing operation within the radiated field strength limits specified in the FCC regulations.

The typically short communication range (e.g., 10 m or less) may also allow for simple radio protocols to be implemented in a Receiver of System 1200, as it may reduce the possibility of transmissions from distant Sensors in System 1200 interfering with Sensors in System 1200 that are closer to the Receiver.

Drive circuitry for a transmitting structure as shown in FIG. 13 may be as simple as using a low frequency microcontroller I/O port output to drive the transmit coil. Such an approach may also be used to reduce transmission power consumption, thereby extending battery life.

If the communication range of a Sensor in System 1200 is under 10 m, it may be imperative that a Receiver in System 1200 be placed near such a Sensor. However, if Sensors are buried or in other intervening media, their location may not be visually apparent. In some applications such as monitoring structures or other forms of construction, the locations of such Sensors may be readily marked. In many agricultural settings, though, this is not easily achieved as fields are often tilled through the use of heavy equipment, thereby making the use of flagging or other Sensor markings unattractive due to the potential of accidental damage. Thus, in such situations, it may be desirable to allow the removal of Receivers in System 1200 from the field when the field is either fallow or when intensive cultural operations occur such as tilling, harvesting, spraying, etc.

With respect to FIG. 14, a method 1400 is shown for tracking sensor locations. At step 1410, at the time of system install, a differential GPS system may be used to reliably locate the Sensor placement to within a few centimeters by placing a GPS base station at a known location on a site, such as a farm, and using a roving GPS receiver during the install. At step 1420, the locations of Sensors and Receivers in System 1200 may be stored in a database. At step 1430, the Receivers in System 1200 may then be removed from the field (while leaving the Sensors of System 1200 buried). At step 1440, the locations of Sensors and Receivers in System 1200 may be retrieved at the time of redeployment. At step 1450, the Receivers in System 1200 may then be reinstalled to within a few centimeters of their original positions.

Method 1400 may be made more convenient by using a mobile phone application. Such an application may possibly be a bar code reader in communication with the GPS roving unit via Bluetooth or other short range wireless communication, thereby allowing for a quick and easy installation of Sensors in System 1200 and recording of their location in a database. For example, a Sensor or Receiver in System 1200 may be scanned by the bar code reader, then installed in its location, and then the GPS roving unit is positioned in place, and the user hits a “capture” sequence that then combines the location with the unit serial number and can then populate a database with the location of a Sensor or Receiver based on its bar code.

In other embodiments, if Sensors in System 1200 have both transmit and receive functionality, then various methods known in the art may be used to store a rough location of the Sensors in a database (e.g., non-differential simple GPS, sighting lines, or by triangulating distances from fixed, prominent locations to get within 5-10 m of the Sensor). A field unit may be used transmit a steady beacon of commands the Sensor may be capable of receiving, which may then be used to trigger a responding transmission from the Sensor, thereby allowing one to systematically scan an area and estimate the Sensor's precise location. As with differential GPS, a mobile application can greatly facilitate this process.

In some configurations of System 1200, it may be possible to mount Receivers on moving structures such as center pivots, lateral irrigation lines, field equipment such as tractors, combines or other vehicles. In such configurations, Sensors with receive capability may be interrogated by a Receiver and triggered into making a measurement and sending the collected data. As such, roving vehicles such as vehicles, tractors, and drones may be used to collect the data by moving to the Sensor location, issuing a command to cause the Sensor to make a measurement, receiving the message (and either storing it for future download or sending via an alternative communication approach such as a cellular modem).

As another example of specific adaptation of System 1200, a center pivot may be equipped with one or more Receivers along its irrigation line, which then may move in known, repeatable paths across a field. The rate of motion of these structures is generally quite slow (roughly of the scale of 1 meter per minute). This may allow a Sensor to make measurements and transmit the results based on the transmission range of the Sensor and the speed of the structure. For example, if the range of a Sensor is 5 m and a center pivot moves an irrigation line at 1 meter per minute, the Sensor may be instructed to transmit at intervals of slightly under 10 minutes. When the center pivot stops, it may be placed in a location near numerous Sensors to allow for collecting data from a limited number of Sensors.

To transmit information from the Sensor to the Receiver (or vice-versa) a communication protocol may be implemented. While parallel communication schemes may be utilized, in the preferred embodiments data may be sent using standard asynchronous schemes using an idle period to separate data packets, and a transition to mark the data packet start, followed by a data packet, and any error checking protocol, and a stop bit followed by another idle period marking the end of the packet. Both Sensors and Receivers in System 1200 may have local clock signals accurate to within better than 5% which may allow appropriate time-framing of the received signals. Possible modulation schemes include phase, amplitude, or frequency shift keying.

In noisy environments, particularly when using Sensors in System 1200 without a receive capability, the detection, as well as correction, of errors is desirable. This may be implemented using simple parity bits to detect a single bad bit or involve more sophisticated forward error correction routines.

In some embodiments, Sensors in System 1200 may use a dynamic Sensor data transmission scheme to only sends data packets only if specific criteria have been met since the last Sensor transmission. Criteria that may be implemented include only sending transmissions when there has been a significant change in one or more measured values since the last transmission, a threshold in one or more values has been breached, etc. This may allow the Sensors in System 1200 to sample more frequently but only transmit values when the measured properties are changing. This can help conserve power on Sensors as well as Receivers in System 1200. For example, a Sensor in System 1200 with soil moisture measurement capability may be programmed to measure soil moisture once every minute, but only to transmit data if the soil moisture change exceeds a threshold. This approach allows such a Sensor to transmit data at frequent intervals during periods of dynamic change in soil moisture (such as during irrigation events) so as to determine important information about water percolation rates, while simultaneously suppressing redundant transmission of data during static soil moisture conditions. In some embodiments, the criteria used to determine whether to send a transmission or not may include a requirement to always transmit a packet at least once in a specified time interval. This may be necessary where a lack of communication from a Sensor in System 1200 may be interpreted as a Sensor that is no longer in operation.

In some embodiments, when more than one Sensor is deployed within communication range of a Receiver in System 1200, Sensor transmissions may need to be identified by the particular Sensor that sent the transmission. This may be implemented by giving each sensor a unique serial number. If the Sensor transmit range is small in System 1200 (e.g., a few meters), an abbreviated Sensor identifier may be used to resolve which Sensor transmission corresponds to which physical Sensor. For example, a small 4-bit identifier may be used to identify 16 different Sensors within range of one Receiver in System 1200. A mobile application may also be used to verify that for each Receiver in System 1200, no duplicates in the 4-bit identifier occur within the range of said Receiver. Additionally, Sensors in System 1200 may be configured to periodically, such as at infrequent intervals, to transmit the short identifier along with a full serial number, which may allow a Receiver in range to associate the unique Sensor serial number with the short identifier.

In other embodiments, particularly those with Sensors having a transmit only capability, if Sensors report at fixed time intervals in System 1200, a Sensor may occasionally move into synch with another Sensor as their local time keeping clocks drift. In order to avoid repeated transmissions colliding, a fixed reporting interval may be varied by a small pseudo-random time. This may be based on adding or subtracting a random time small compared to the transmit interval. This random number may be generated in a number of ways including using the Sensor serial number to generate a random number sequence.

In embodiments where Sensors in System 1200 have a receive capability, the Receivers may time synch the Sensor response times so that each Receiver can be in a low power state except during Sensor transmissions. In addition, a Receiver in System 1200 may modify the Sensor(s) reporting interval or other behavior in response to system-wide operating conditions that are communicated to the Receiver, such as via an external network interface. For example, when crops are fallow, the Receiver may be informed of this state, and then direct itself, as well as the Sensors connected to it into a very low power, sparse reporting mode to conserve battery life during these periods when irrigation is not occurring. In further embodiments, a Receiver in System 1200 may monitor a Sensor transmission signal level and error rate (when employing FEC or parity bits) and if the signal is too low or noisy, such a Receiver may direct a Sensor to increase transmit power levels or use stronger error correction techniques.

To conserve power on a Receiver in System 1200 and to facilitate long battery life or allow operation with small solar power panels, in some embodiments a low power scheme may be implemented using a very low power transmission detection circuit. In such embodiments, while waiting for Sensor transmissions, only the low power transmission detection circuitry may be powered in a Receiver of System 1200. A Sensor may then transmit a simple, short “wake-up” signal that the Receiver may then detect and use to wake up the full Receiver circuitry. A short interval after a Sensor issues the “wake-up” signal, a full Sensor transmission may occur. After receipt of this Sensor transmission, the Receiver may either store the packet for later transmission or send it via a network interface, and then return to the very low power mode.

In such embodiments, if the Sensors of System 1200 have a receive capability, the Receiver may use synch signals to schedule Sensor transmission into known windows, thereby allowing the Receiver to be in a low power mode except during these short Sensor reception windows. The Receiver may periodically resynch the Sensors in order to correct for time keeping errors in the Sensor and Receivers.

In further embodiments, a Receiver in System 1200 may be used to prioritize data transmissions via its network interface depending on preset algorithms. For example, such a Receiver may only be instructed to send summary data, such as statistics showing just the high and low readings over a time interval or other condensed metrics. In addition, depending on user configuration or management operations, the frequency and detail of the data that is sent by such a Receiver via its network interface may be modified. For example, during irrigation operations, instructions may be followed that require such a Receiver to send the full Sensor data at short intervals so as to provide better data that allows for determining optimum irrigation run times.

As an example of specific embodiment of System 1200, a group of 10 Receivers may be distributed over a 400-acre farm. Each Receiver may be installed near 12 Sensors buried in the soil that are capable of measuring soil moisture, salinity, and temperature. The Sensors may be transmit-only sensors employing a 4 bit identifier, and a 4 byte sensor measurement packet. The operating frequency of the Sensors may be 173 kHz with a 1200 baud transmit rate using an asynchronous amplitude shift key modulation. Simple FEC employing a Hamming 7,4 scheme capable of detecting one or two bit errors and correcting a single bit error may be employed. The total Sensor packet with FEC of such an arrangement if employed is approximately 10 bytes occupying about 0.2 s of transmit time.

The Sensors may be set to measure soil moisture, salinity, and temperature at 2 minute intervals and transmit current readings at least once every 20 minutes or if any measured value changes by more than a preset limit since the last Sensor transmission. The Sensors may also transmit a short “wake-up” signal to the Receivers 0.2 s before actual Sensor data transmission begins.

The Receivers may be battery powered with low power “wake-up” signal detection circuitry to monitor the 173 kHz Sensor transmissions. Generally, the Receivers may be in a low power mode monitoring for a “wake-up” signal and then switch into a more power intensive receive mode. The Receivers may have sufficient memory to allow up to one month of Sensor data to be stored on the Receiver and may have a cellular modem for uplink to Data Analytic Component 1250, which may reside on a cloud server environment. Unless a signal is received by the Receiver from Data Analytic Component 1250, all collected data may be sent at 10 minute intervals.

All collected Sensor data may be stored via Data Analytic Component 1250 and may be made available to users and applications via a fixed computer as well as mobile phones and tablets. Data visualization, storage, and analysis features may be implemented in Data Analytic Component 1250 and available to all platforms via an Application Programming Interface (API). Data Analytic Component 1250 may also have access to third party data streams such as peak power rates, weather forecasts, water district restrictions, etc. to further refine data presented to a user.

Based on sensor data and other information stored by Data Analytic Component 1250, various reports or analysis may be generated such as

1) Suggesting upcoming irrigation schedule. 2) Current and historical conditions in the field. 3) Metrics indicative of irrigation efficiency and salinity management. 4) Metrics indicative of determined soil properties such as field capacity, water percolation rates, etc. 5) Alarms indicating conditions outside of preset limits. 6) Forward looking irrigation demands based on current conditions and future weather forecasts. 7) Plant stress indices (dependent on moisture and salinity levels). 8) System deployment metrics (missing Sensor packets, equipment health, hardware locations, battery levels, etc.) 9) Account management aspects such as authorized users, passwords, user configurations, etc.

Data Analytic Component 1250 may also support a mobile installation application that captures GPS locations, hardware serial numbers, and identifies which Sensors are located within receive distance of the Receivers. Data Analytic Component 1250 may also support a mobile installation application that provides client information, any additional information such as irrigation water quality data, soil maps, irrigation equipment type, etc.

During the growing season, a client may use System 1200 to monitor current soil moisture conditions and to schedule irrigation times and the amount of water to be applied. During irrigation, the Sensors may report data at a shorter interval allowing interactive control of the irrigation, such as terminating irrigation at a certain moisture threshold. Tracking the temperature and moisture conditions with the Sensors in System 1200 across the season may allow for identifying disease issues such as mold, leaf rot, as well as when weed germination may occur and when optimum planting times should occur. Salinity may be tracked via System 1200 and when salinity builds to levels that will impact plant health/yield, intense irrigation to flush salts below the root zone may be triggered and Sensor data may be used to monitor the efficacy of the flushing operation. At harvest, the map of yield may be compared to actual Sensor data to help highlight areas of low or high yield and modify future behavior to increase yield.

In locations such as greenhouses, grow operations, arboretums, interior planters, etc. the Receiver pickup coils used in System 1200 may be quite large and built into the floor, growing tables, or planter boxes and shelves. This may allow individual pots or planter boxes to have Sensors in them and when placed in these areas, Sensor data may be received to manage operations down to the single plant level if desired. Similarly, this approach may be implemented on roadways or in structures such as buildings, dams, and canals to monitor moisture, cement curing, or other important parameters.

In some embodiments, Data Analytic Component 1250 may also receive information relating to the amount of water supplied over time to an area being measured by a Sensor. For example, in an application where Sensors are embedded in a levee, Data Analytic Component 1250 may receive information indicating the water level or flow rate of water near each Sensor. In such an embodiment, Data Analytic Component 1250 may use information about the amount of water being applied to each Sensor to make predictions about future moisture levels or salinity. In further embodiments, Data Analytic Component 1250 may also utilize weather forecast information in making predictions about future moisture levels or salinity. In other embodiments, such as where the water being applied in the area of each sensor is controllable (e.g., an irrigation system), Data Analytic Component 1250 may use information about the amount of water that has been applied to an area around each Sensor and also weather forecast information to calculate irrigation schedules.

In addition to embodiments described using software, various embodiments may also be implemented primarily in hardware using, for example, components such as application specific integrated circuits (“ASICs”), or field programmable gate arrays (“FPGAs”). Implementation of a hardware state machine capable of performing the functions described herein will also be apparent to those skilled in the relevant art. Various embodiments may also be implemented using a combination of both hardware and software.

Furthermore, those of skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and method steps described in connection with the above described figures and the embodiments disclosed herein can often be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled persons can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the invention. In addition, the grouping of functions within a module, block, circuit, or step is for ease of description. Specific functions or steps can be moved from one module, block, or circuit to another without departing from the invention.

Moreover, the various illustrative logical blocks, modules, and methods described in connection with the embodiments disclosed herein can be implemented or performed with a general purpose processor, a digital signal processor (“DSP”), an ASIC, FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor can be a microprocessor, but in the alternative, the processor can be any processor, controller, microcontroller, or state machine. A processor can also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

Additionally, the steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination thereof. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium including a network storage medium. An exemplary storage medium can be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can also reside in an ASIC.

The above description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles described herein can be applied to other embodiments without departing from the spirit or scope of the invention. Thus, it is to be understood that the description and drawings presented herein represent presently preferred embodiments and are therefore representative of the subject matter which is broadly contemplated by the present invention. It is further understood that the scope of the present invention fully encompasses other embodiments that may become obvious to those skilled in the art and that the scope is accordingly not limited.

While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not of limitation. The breadth and scope should not be limited by any of the above-described exemplary embodiments. Where this document refers to technologies that would be apparent or known to one of ordinary skill in the art, such technologies encompass those apparent or known to the skilled artisan now or at any time in the future. In addition, the described embodiments are not restricted to the illustrated example architectures or configurations, but the desired features can be implemented using a variety of alternative architectures and configurations. As will become apparent to one of ordinary skill in the art after reading this document, the illustrated embodiments and their various alternatives can be implemented without confinement to the illustrated example. One of ordinary skill in the art would also understand how alternative functional, logical or physical partitioning and configurations could be utilized to implement the desired features of the described embodiments.

Furthermore, although items, elements or components may be described or claimed in the singular, the plural is contemplated to be within the scope thereof unless limitation to the singular is explicitly stated. The presence of broadening words and phrases such as “one or more,” “at least,” “but not limited to,” or other like phrases in some instances shall not be read to mean that the narrower case is intended or required in instances where such broadening phrases may be absent.

All references cited herein are expressly incorporated by reference. 

What is claimed is:
 1. A system for soil parameters, comprising: an oscillator for generating an AC signal; a bandpass filter coupled to said oscillator for filtering said AC signal; a first capacitor coupled to said bandpass filter for blocking DC components in said AC signal; a first resistor coupled to the output of the first capacitor for applying a bias voltage to said AC signal; a first AC meter coupled to the output of the first capacitor; a second resistor coupled to the output of the first capacitor; a second AC meter coupled to the output of the second resistor; a third resistor coupled to the output of the second resistor; a third AC meter coupled to the output of the third resistor; and a second capacitor coupled to the output of the third resistor for blocking any bias voltage that may have been applied to said AC signal and wherein said second capacitor is capable of being coupled to an electrical conducting structure.
 2. The system of claim 1, wherein the first, second, or third AC meter is composed of a diode, wherein the AC signal input is applied to said diode; a DC meter coupled to the output of the diode for measuring a DC voltage; and a parallel RC circuit coupled between the output of the diode and ground.
 3. The system of claim 1, wherein the first, second, or third AC meter is composed of a transistor, wherein the AC signal input is applied to the base of said transistor; a DC meter coupled to the emitter of the transistor for measuring a DC voltage; and a parallel RC circuit coupled between the emitter of the transistor and ground.
 4. The system of claim 1, further comprising a bias voltage source coupled to the first resistor.
 5. The system of claim 1, further comprising a control circuit capable of turning on or off the oscillator, determining a set of AC voltages via the first, second, and third AC Meters, and performing computations based on the set of AC voltages to determine moisture level or salinity.
 6. The system of claim 5, further comprising a communications circuit for sending or receiving data via the control circuit.
 7. The system of claim 6, wherein the communications circuit utilizes a tuning capacitor and wire coil.
 8. A system for monitoring moisture level or salinity, comprising: a set of remote sensors, wherein each remote sensor has the capability to transmit data; a monitoring device having the capability to determine a location based on radio navigation and to receive the data from each remote sensor; and a data analytic component that records the location of each sensor and processes the data from each remote sensor.
 9. The system of claim 8, further comprising a vehicle that is programmed to travel to each remote sensor location to receive data.
 10. The system of claim 8, wherein the data from each remote sensor contains information regarding at least one of a moisture level, salinity, or temperature recorded by the remote sensor.
 11. The system of claim 10, wherein the data analytic component based on the data obtained from the set of remote sensors is capable of calculating an irrigation schedule, providing current and historical conditions for each remote sensor, or triggering an alarm if the moisture level, salinity, or temperature recorded by at least one remote sensor is outside of predetermined limits.
 12. The system of claim 11, wherein the data analytic component is further capable of receiving water supply metrics with respect to each remote sensor that indicates an amount of water applied to a predetermined area that is expected to affect the measurements of each remote sensor.
 13. The system of claim 12, wherein the data analytic component is further capable of receiving weather forecast information.
 14. The system of claim 13, wherein the data analytic component is capable of calculating an irrigation schedule based on the water supply metrics, the weather forecast information, and the data from each remote sensor.
 15. The system of claim 13, wherein the data analytic component is capable of predicting a future moisture level or salinity for each remote sensor based on the water supply metrics, the weather forecast information, and the data from each remote sensor.
 16. A method for monitoring moisture level or salinity comprising: receiving data from a set of remote sensors regarding moisture level, salinity, and temperature at each remote sensor; receiving location data for each remote sensor; receiving weather forecast information; and receiving water supply metrics with respect to each remote sensor that indicates an amount of water applied to a predetermined area that is expected to affect the measurements of each remote sensor.
 17. The method of claim 16 further comprising calculating at least one of an irrigation schedule, current and historical conditions for each remote sensor, or a prediction of a future moisture level or salinity for each remote sensor.
 18. The method of claim 17 further comprising instructing a vehicle to travel to each remote sensor.
 19. The method of claim 18 wherein instructing the vehicle includes providing a specific time that the vehicle should be located at each remote sensor. 